Mysteriously rapid rise in Legionnaires’ disease incidence correlates with declining atmospheric sulfur dioxide

Abstract Legionnaires’ disease (LD) is a severe form of pneumonia (∼10–25% fatality rate) caused by inhalation of aerosols containing Legionella, a pathogenic gram-negative bacteria. These bacteria can grow, spread, and aerosolize through building water systems. A recent dramatic increase in LD incidence has been observed globally, with a 9-fold increase in the United States from 2000 to 2018, and with disproportionately higher burden for socioeconomically vulnerable subgroups. Despite the focus of decades of research since the infamous 1976 outbreak, substantial knowledge gaps remain with regard to source of exposure and the reason(s) for the dramatic increase in LD incidence. Here, we rule out factors indicated in literature to contribute to its long-term increases and identify a hitherto unexplored explanatory factor. We also provide an epidemiological demonstration that the occurrence of LD is linked with exposure to cooling towers (CTs). Our results suggest that declining sulfur dioxide air pollution, which has many well-established health benefits, results in reduced acidity of aerosols emitted from CTs, which may prolong the survival duration of Legionella in contaminated CT droplets and contribute to the increase in LD incidence. Mechanistically associating decreasing aerosol acidity with this respiratory disease has implications for better understanding its transmission, predicting future risks, and informed design of preventive and interventional strategies that consider the complex impacts of continued sulfur dioxide changes.


Supporting Information Text
Aqueous chemistry model to calculate SO2 uptake and pH of a cooling tower (CT) water droplet.Similar to those in clouds or fog, the uptake of sulfur dioxide (SO2) by CT droplets involves several chemical reactions that occur in the aqueous phase.When SO2 comes into contact with CT droplets, it can undergo dissolution and subsequent reactions, leading to the formation of sulfate and decrease of pH values of the droplet.The SO2 aqueous chemistry process has been well described in textbooks on atmospheric chemistry (1,2) and has been implemented in various models of atmospheric chemistry.In this study, we use the SO2 aqueous chemistry module (3)(4)(5) of the widely-used community atmospheric chemistry model GEOS-Chem (6).The main processes are briefly summarized here.
2. Hydration: SO2 (aq) can undergo hydration reactions, forming bisulfite ions (HSO3 -) and sulfite ions (SO32 -): The oxidation by dissolved H2O2 (R2) is independent of pH while that by dissolved O3 (R3) is significant only at pH > 6. Sulfur in the aqueous phase can also be oxidized by other less important oxidants (such as O2, NO2, NO3, etc.).In this work, we only consider the aqueous phase oxidation of SO2 by H2O2 and O3.
We calculate cloud water pH iteratively by using the concentrations of sulfate, total ammonium (ammonium + ammonia), total nitrate (nitrate + nitric acid), SO2, and CO2 based on their effective Henry's law coefficients and cloud liquid water content (7,8), with the iterative calculation updated to use Newton's method (9).The initial pH of droplets is assumed to be that of pure water in equilibrium with CO2 and is the same for all case years (with different SO2 concentrations).After about 30 s of exposure to SO2 at concentrations typical of those in New York State during the last two decades, the pH of CT droplets is dominated by the SO2 taken up and is insensitive to initial pH concentrations.
Breaking the association between [SO2] and LD cases.In Fig. 3C (main text), the opposite relationship between LD cases per week and 1-week-lagged [SO2] was demonstrated.The analysis is repeated on randomized [SO2] data (Fig. S1A).No association between [SO2] and LD case count exists in this case.Additionally, the same analysis (as in Fig. 3C; main text) is conducted for the SPARCS-reported legionellosis hospitalizations (Fig. S2A) and with the randomization of [SO2] (Fig. S1B).We use the ICD-10 codes "A481" & "A482" and ICD-9 codes "48284" & "04089" to identify legionellosis hospitalizations.These sensitivity analyses confirm that the properties of the [SO2] distribution do not affect the relationship obtained in Fig. 3C (main text).
[SO2] and cases of LD and reference diseases.For further sensitivity analyses (Fig. S2), we use SPARCS-reported hospitalizations to compare the [SO2]-disease relationship found for Legionnaire's disease (Fig. S2A) with that for three other diseases: acute appendicitis (Fig. S2B), streptococcal pneumonia (Fig. S2C ), and chronic ischemic heart disease (Fig. S2D).We find no such relationship for these three diseases, in fact the opposite relationship (increasing SO2 and increasing hospitalizations) is seen for ischemic heart disease and pneumonia (in line with literature), further confirming the solidity of our findings in Fig. 3C.
Distance to nearest cooling tower and cases of LD and reference diseases.In Fig. 4D (main text), we summarize the relationship between LD hospitalizations and distance to the nearest cooling tower.Figure S3 demonstrates this for LD (Fig. S3A) and the three other diseases (Fig. S3B-D) without any normalization of the distance.For LD, hospitalizations increase from 2000-2004 to 2015-2019, unlike for the other diseases.Additionally, the median distance to the nearest CT (denoted by dashed vertical line segment) becomes smaller with time.This indicates that more of the increase from 2000-2004 to 2015-2019 occurs closer to the nearest CT, consistent with the observation in Fig. 4D (main text) that the rate of increase of LD hospitalizations is greater closer to the nearest CT.At first glance, it may seem as though there are low number of cases closer to the cooling tower, with peak values around 600-900 m.This is due to the inappropriate discretization of the distance, which captures the contrasting effects of larger distances from a CT covering larger annular areas and thus larger populations (or its diseased subsets) versus the reducing probability of being far away from a cooling tower.
Adjusting for this effect, the analysis in Fig. 4D (main text) is extended in Fig. S4.The first and last panels of the top row of Fig. S4 is equivalent to Fig. 4D (main text), with quintiles for acute appendicitis hospitalizations with distance to the nearest cooling tower being used to bin LD hospitalizations.The same is shown using quintiles for chronic ischemic heart disease (center row) and quintiles for streptococcal pneumonia (bottom row).This sensitivity analysis shows that the finding for LD hospitalization relationship with cooling tower location is consistent.Non-flattening of the curve at large distance for LD with respect to pneumonia is speculated to be due to the lower transmission probability in remote areas (which have the nearest cooling tower farther away).In any case, streptococcal pneumonia is not an ideal reference as described earlier.

Fig. S1 .A
Fig. S1.Association between [SO2] and Legionnaires' disease.(cf.Fig. 3C) Binned scatterplot for weekly comparison of (A) CDC-reported LD cases and (B) SPARCS-reported LD hospitalizations with 1-week-lagged randomized [SO2] for Erie and Nassau.Red circles show average LD cases per week with respect to 1-week-lagged [SO2].Red curve shows generalized additive model fit and red shading shows its 95% confidence interval.Color scale (log10) indicates frequency of weeks.Bins are discretized non-linearly along the x-axis such that the [SO2] distribution is uniform.